A Study on the Detection of Defects in Solar Cells and Modules in EL Images based on Artificial Intelligence

Currently, investment is being made in renewable energy for the transition to a low-carbon economy and society, and interest in solar energy is also increasing. In addition to the technological development of solar cells and photovoltaic (PV) modules, research in the field of convergence with artificial intelligence technology is being actively conducted. Defects occurring in the manufacturing process of solar cells and modules can be detected through electroluminescence (EL) measurements. In this study, we propose an artificial intelligence technology that can automatically detect defects in cells and modules in real time using EL image data of solar cells and modules in the manufacturing process. For EL defect detection, we propose an algorithm with high suitability in terms of speed and accuracy by applying deep learning-based object detection models and comparing and evaluating detection performance. In the case of the YOLO (you only look once) algorithm, which belongs to a one-step detector, it learns In the case of the YOLO (you only look once) algorithm, which belongs to a one-step detector, it learns through an optimization process to find information about the defect and the location information of the corresponding failure in the form of a bounding box, and then detects the failure based on this information. The introduction of a deep learning-based defect detection model in the manufacturing process is expected to reduce the time required for defect detection by solar cell and PV module manufacturers and contribute to productivity improvement.